Socioeconomic inequalities in secondhand smoke exposure before, during and after implementation of Quebec’s 2015 ‘An Act to Bolster Tobacco Control’
Bibliographic record
Abstract
Background To better understand whether tobacco control policies are associated with changes in secondhand smoke (SHS) exposure across socioeconomic groups, we monitored differences in socioeconomic inequalities in SHS exposure in households and private vehicles among youth and adults before, during and after adoption of Quebec’s 2015An Act to Bolster Tobacco Control. Methods Using data from the Canadian Community Health Survey, we examined the prevalence of daily exposure to SHS in households and private vehicles among youth (ages 12 to 17) and adults (ages 18+) across levels of household education and income (separately) in 2013/2014, 2015/2016 and 2017/2018. We tested differences in the magnitude of differences in outcomes over time across education and income categories using logistic models with interaction terms, controlling for age and sex. Results We detected inequalities in SHS exposure outcomes at each time point, most markedly at home among youth (OR of SHS exposure among youth living in the 20% poorest households vs the 20% richest=4.9, 95% CI 2.7 to 6.2). There were decreases in SHS exposure in homes and cars in each education/income group over time. The magnitude of inequalities in SHS exposure in homes and cars, however, did not change during this period. Conclusions The persistence of socioeconomic inequalities in SHS exposure despite implementation of new tobacco control laws represents an increasingly worrisome public health challenge, particularly among youth. Policymakers should prioritise the reduction of socioeconomic inequalities in SHS exposure and consider the specific needs of socioeconomically disadvantaged populations in the design of future legislation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".